Abstract
The average kappa coefficient of a binary diagnostic test is a measure of the beyond-chance average agreement between the binary diagnostic test and the gold standard, and it depends on the sensitivity and specificity of the diagnostic test and on disease prevalence. In this manuscript the estimation of the average kappa coefficient of a diagnostic test in the presence of verification bias is studied. Confidence intervals for the average kappa coefficient are studied applying the methods of maximum likelihood and multiple imputation by chained equations. Simulation experiments have been carried out to study the asymptotic behaviors of the proposed intervals, given some application rules. The results obtained in our simulation experiments have shown that the multiple imputation by chained equations method provides better results than the maximum likelihood method. A function has been written in R to estimate the average kappa coefficient by applying multiple imputation. The results have been applied to the diagnosis of liver disease.
Highlights
A binary diagnostic test (BDT) is a medical test used to determine whether or not a patient has a certain disease
When considering the losses associated with a misclassification with the BDT, the effectiveness of a BDT is measured with the weighted kappa coefficient [1,2], which depends on Se and Sp of the BDT, on the disease’s prevalence and on a weighting index, which is a measure of the relative loss between the false positives and the false negatives and it is a value set by the clinician and takes a value between 0.5 and 1 when the BDT is used as a screening test, and the weighting index takes a value between 0 and 0.5 when the BDT is used as a confirmatory test
The average kappa coefficient depends on the Se and Sp of the BDT and on the disease prevalence, but it does not depend on the weighting index; the average kappa coefficient solves the problem of assigning values to the weighting index
Summary
A binary diagnostic test (BDT) is a medical test used to determine whether or not a patient has a certain disease. If the GS is an expensive medical test or a medical test that involves risks for the patient, the GS is not applied to all the patients in the sample In this situation, if Se and Sp are estimated without considering the patients for whom the GS is unknown, the estimators are affected by so-called verification bias [4,5]. Roldán-Nofuentes and Luna [8] have studied the estimation of the weighted kappa coefficient in the presence of partial disease verification.
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